Analysis of LSF frame selection in voice conversion

In practical applications of voice conversion, it is necessary to be able to cope with small amounts of speaker-specific training data. Consequently, most of the proposed voice conversion algorithms are based on probabilistic conversion functions. Recently, however, there has been increased interest in unit selection based approaches for voice conversion. It is evident that typical training sets are too small for enabling meaningful selection of large units such as diphones. But would it be possible to use smaller segments like frames for high quality results provided that the selection is handled very well? In this paper, we analyze the performance of the frame selection approach in ideal conditions. In the experiments, line spectral frequencies of test sentences are replaced with the best matches from different training sets. The results show that perceptually transparent quality cannot be achieved with realistic database sizes.